Re: Signs and Symptoms of Myofascial Pain: An International Survey of Pain Management Providers and Proposed Preliminary Set of Diagnostic Criteria
Bibliographic record
Abstract
Dear Editor, We read the article of Rivers et al. [1] with great interest, and we find it a valuable contribution to the literature on the diagnosis of myofascial pain syndrome (MPS). However, we would like to comment upon two issues with respect to this article. The first issue we noticed is that the proposed diagnostic criteria [1] do not require identification of a myofascial trigger point (MTrP). While the criteria require appreciation of a tender point and recognition of pain, this is not the same as an MTrP. There are of course many other etiologies of areas tender to palpation, such as the tender areas of fibromyalgia. Considering that the presence of an actual MTrP is what differentiates MPS from other disorders and that MTrPs are the focus of most therapies for MPS, relegating the appreciation of an MTrP to a supporting symptom only seems to add confusion. We believe that pathophysiologically the presence of an MTrP is central to and a necessary feature for the diagnosis of MPS. Therefore, the discrepancy is a demonstration that while practitioner opinion is valuable, it will not always result in optimal decisions. This might be a result of the relative lack of knowledge of the literature for the surveyed physicians. While clinicians understandably don’t require the presence of an actual trigger point for their clinical diagnosis of MPS given the reproducibility difficulties of palpation [2,3], a researcher in the field may prefer to look to solving this problem with an imaging modality such as ultrasound rather than simply adopting the best criteria of a bad lot. The other issue that arose is that the survey administered does not appear to have questioned physicians about the “local twitch response” (LTR): the appearance of muscular twitch upon manual stimulation of the trigger point. Why is this important? It is a widely used criterion in research of MPS and MTrPs [4]. The LTR was included in 44% of existing research reports as of 2007. The LTR is more often mentioned as a criterion in research than the finding of a tender spot (without a nodule) (mentioned in 15% of reports) and reduced range of motion (mentioned in 22% of reports), both of which were included in this survey [1]. Interestingly, a tender spot without a nodule was one of only two criteria viewed as essential by respondents in the survey despite its relatively low usage in the research literature, which makes us wonder what respondents would have responded about the previously more utilized LTR criterion. In addition to its usage in research criteria, we have access to not yet published data from a survey of Canadian physicians that found the majority of clinicians viewed the local twitch response as a criterion for a diagnosis of myofascial pain syndrome (Unpublished Manuscript, L. Grosman-Rimon, H. Clarke P.B. Mills, Aaron Kin-Yun Chan, D. Kumbhare). This leads us to believe that this criterion may have received a strong result in the Rivers survey had it been included. Interestingly, Rivers et al. had a free response section, for any additional signs and symptoms that had not been directly asked about, and this did not reveal the lack of a twitch response option [1]. This indicates either an inadequacy of this approach to identifying missing questions, or indicates that clinicians may only remember or consider important the local twitch response when prompted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".